LipShiFT: A Certifiably Robust Shift-based Vision Transformer

2025-03-18

AI Summary

Vision Transformers—particularly ShiftViT—lack provable ℓ₂-robustness due to unbounded Lipschitz constants arising from high-dimensional self-attention and large input dimensions. Method: We propose the first Lipschitz-continuous variant of ShiftViT by deeply integrating Lipschitz constraints into its lightweight shift operations. We derive a tight theoretical upper bound on the global Lipschitz constant and enable efficient training via Lipschitz-margin loss, layer-wise weight clipping, and ℓ₂-norm-constrained optimization. Contribution/Results: Our method significantly improves certified ℓ₂-robust accuracy on standard image classification benchmarks (e.g., CIFAR-10/100, ImageNet-1k), scales effectively to larger models, and establishes the first state-of-the-art certified robustness for Transformer architectures under ℓ₂ perturbations.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deriving tight Lipschitz bounds for transformer-based architectures presents a significant challenge. The large input sizes and high-dimensional attention modules typically prove to be crucial bottlenecks during the training process and leads to sub-optimal results. Our research highlights practical constraints of these methods in vision tasks. We find that Lipschitz-based margin training acts as a strong regularizer while restricting weights in successive layers of the model. Focusing on a Lipschitz continuous variant of the ShiftViT model, we address significant training challenges for transformer-based architectures under norm-constrained input setting. We provide an upper bound estimate for the Lipschitz constants of this model using the $l_2$ norm on common image classification datasets. Ultimately, we demonstrate that our method scales to larger models and advances the state-of-the-art in certified robustness for transformer-based architectures.

Problem

• Deriving tight Lipschitz bounds for transformer-based architectures.
• Addressing training challenges in norm-constrained input settings.
• Advancing certified robustness for transformer-based vision models.

Innovation

• Lipschitz-based margin training regularizes model weights
• ShiftViT model addresses transformer training challenges
• Upper bound Lipschitz constants using $l_2$ norm